Function reference • cypro

Object Initiation Three functions are needed to set up a fully functional cypro object.
`designExperiment()`	Object initiation: Step 1
`loadDataFile()` `loadDataFiles()`	Object initiation: Step 2
`processData()`	Object initiation: Step 3
Object Summary and Information Keep track of progress and set up by printing summaries in the console.
`printSubsetHistory()`	Print subset information
`printSummary()` `printAnalysisSummary()`	Print object summary
Object Loading and Saving Some handy functions that make saving and loading corresponding objects more convenient.
`saveCyproObject()`	Save cypro object
Object Subsetting Create cypro object from data subsets for more in depth analysis.
`subsetByCellId()`	Create data subset by cell ids
`subsetByCellLine()`	Create data subset by cell lines
`subsetByCluster()` `subsetByGroup()`	Create data subset by cluster
`subsetByCondition()`	Create data subset by conditions
`subsetByFilter()`	Create data subset by specified requirements
`subsetByNumber()`	Create data subset by reducing the number of cells
`subsetByQuality()`	Create data subset according to coverage quality
Object Manipulation cypro invites you to extract your analysis and to add the results of your analysis. Constant change of the object’s content is therefore inevitable. The following functions serve as handy, helping hands to add cypro extern content to the object without disturbing it’s integrity regarding cypro intern processes.
Set Content The set-functions let you set the content of specific slots.
`setCellDf()`	Set cell data.frame
`setClusterConv()` `setDimRedConv()` `setCorrConv()`	Set analysis objects
`setDefaultInstructions()`	Set cypro default
`setGroupingDf()`	Set data data.frames
`setStorageDirectory()`	Set default storage directory
Add Content The add-functions let you add content to the cypro object.
`addClusterVariables()` `addMetaVariables()`	Add discrete/categorical variables that group the cells
`addHierarchicalClusterVariables()`	Add hierarchical clustering results to overall data
`addKmeansClusterVariables()`	Add kmeans clustering results to overall data
`addPamClusterVariables()`	Add PAM clustering results to overall data
`addStatVariables()`	Add numeric variables
`addVariableSet()`	Add predefined set of variables
Discard Content The discard-functions let you delete unwanted information savely.
`discardClusterVariables()` `discardMetaVariables()`	Discard unwanted variables from your data
`discardDistanceMatrix()`	Discard calculated distance matrix
`discardStatVariables()` `keepStatVariables()`	Discard unwanted variables from your data
Adjust Content The adjust-functions let you adjust content of the cypro object without overwriting it in it’s essence.
`adjustableDefaultInstructions()`	Valid default arguments
`adjustDefaultInstructions()`	Adjust object based default
Rename Content The rename-functions let you adjust the names of data variables and of groups.
`renameClusterDf()` `renameClusterDfWith()`	Rename cluster variables
`renameGroups()`	Rename groups
`renameMetaDf()` `renameMetaDfWith()`	Rename meta variables
`renameStatsDf()` `renameStatsDfWith()`	Rename statistic variables
`renameTracksDf()` `renameTracksDfWith()`	Rename track variables
Extract Data cypro invites you to extract your analysis. get-functions let you access every imaginable aspect of information conveniently.
Data frames A lot of data comes in form of tidy data.frames.
`getBatchEffectDf()` `getBatchEffectDist()`	Obtain batch effect computation results
`getGroupingDf()` `getClusterDf()` `getMetaDf()` `getWellPlateDf()`	Obtain grouping information
`getMissingValuesDf()`	Obtain missing value counts
`getSetUpDf()`	Obtain well plate set up
`getStatsDf()`	Obtain stat data.frame
`getTracksDf()`	Obtain track data.frame.
`getVariableDf()`	Obtain variable centered summaries
`mutateClusterDf()` `mutateMetaDf()` `mutateStatsDf()`	Create and modify data variables
`mutateTracksDf()`	Create and modify data variables
Data variable names and group names Functions that return object dependent input options for different arguments.
`getGroupingVariableNames()` `getClusterVariableNames()` `getMetaVariableNames()` `getWellPlateVariableNames()`	Obtain grouping variable names of cell data
`getStatVariableNames()` `getTrackVariableNames()`	Obtain numeric variables of cell data
`getGroups()` `getGroupNames()`	Obtain group names a grouping variable contains
Names and content of variable sets Keep track of your data variables by gathering them to variable sets.
`getVariableSet()` `getVariableSets()` `getVariableSetNames()`	Obtain defined sets of variables
Analysis results Extract analysis results for cypro extern analysis.
`getHclustConv()` `getHclustObject()` `getKmeansConv()` `getKmeansObject()` `getPamConv()` `getPamObject()`	Obtain cypros clustering objects
`getCorrConv()` `getCorrObject()`	Obtain cypros correlation objects
`getPcaConv()` `getPcaObject()` `getTsneConv()` `getTsneObject()` `getUmapConv()` `getUmapObject()`	Obtain dimensional reduction objects
Outlier detection
`getOutlierResults()` `getOutlierIds()`	Obtain outlier detection results
`getOutlierWells()`	Obtain possible outlier wells
Miscellaneous
`nCells()` `nCellLines()` `nConditions()`	Number of miscellaneous content
`nFiles()`	Number of files read in
`nMissingValuesStats()`	Number of NAs by cell id (in stats)
`nMissingValuesTracks()`	Number of NAs by cell id (in tracks)
Quality Check Clean your data before conducting analysis with common quality checks.
Outlier detection
`detectOutliers()`	Detect outlier cells
`removeOutliers()`	Remove outliers from object
Batch effect detection
`detectBatchEffects()`	Detect batch effects
`plotBatchHeatmap()`	Visualize possible batch effects
Analysis and Profiling Leverage convenient implementations of several machine learning algorithms to cluster and profile your cells.
Dimensional reduction
`runPca()` `runTsne()` `runUmap()`	Compute dimensional reductions
Clustering
`initiateHierarchicalClustering()` `initiateKmeansClustering()` `initiatePamClustering()`	Set up clustering objects with cypro
`computeDistanceMatrices()`	Compute distance matrices
`agglomerateHierarchicalCluster()`	Agglomerate hierarchical cluster
`computeKmeansCluster()`	Compute cluster with kmeans
`computePamCluster()`	Compute cluster with partitioning around medoids (PAM)
Correlation
`initiateCorrelation()`	Set up correlation with cypro
`correlateAll()` `correlateAcross()`	Compute correlation between variables
Visualization and Animation
Statistics and common plots
`plotBoxplot()` `plotDensityplot()` `plotHistogram()` `plotRidgeplot()` `plotViolinplot()`	Plot numeric distribution and statistical tests
`plotStatisticsInteractive()`	Plot statistics related plots interactively
`plotScatterplot()`	Plot a scatterplot
Dimensional reduction
`plotPca()` `plotTsne()` `plotUmap()`	Plot dimensional reduction results
Clustering
`plotScreeplot()`	Plot a scree plot
`plotAvgSilhouetteWidths()` `plotSilhouetteWidths()`	Plot pam cluster quality
`plotPamMedoids()`	Plot medoid results
Timelapse dependent
`plotTimeHeatmap()`	Visualize changes of cell characteristics over time
`plotTimeLineplot()`	Visualize changes of cell characteristics over time
Modules Depending on the experiment set up and the data input different modules (collections of functions) are at your your disposal.
Migration In case of timelapse experiments that come with x- and y-coordinates you can explore cellular migration behaviour.
`plotSingleTracks()`	Plot single cell migration
`plotAllTracks()`	Plot scaled cell migration
`animateAllTracks()`	Animate all tracks
Valid argument input Functions that don’t take any argument specifications but simply return valid input options for several arguments.
`validAgglomerationMethods()` `validColorPalettes()` `validColorSpectra()` `validDistanceMethods()` `validKmeansMethods()`	Valid input options

Reference

Object Initiation

Object Summary and Information

Object Loading and Saving

Object Subsetting

Object Manipulation

Set Content

Add Content

Discard Content

Adjust Content

Rename Content

Extract Data

Data frames

Data variable names and group names

Names and content of variable sets

Analysis results

Outlier detection

Miscellaneous

Quality Check

Outlier detection

Batch effect detection

Analysis and Profiling

Dimensional reduction

Clustering

Correlation

Visualization and Animation

Statistics and common plots

Dimensional reduction

Clustering

Timelapse dependent

Modules

Migration

Valid argument input